{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5cbb55ed-a22b-43be-b479-44ca6c1eaf68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "postgresql+psycopg://hbu:********@127.0.0.1:2346/scorpio\n",
      "环境变量加载完成！\n",
      "数据库连接引擎创建成功！\n"
     ]
    }
   ],
   "source": [
    "# 导入必要的库\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "from sqlalchemy import create_engine\n",
    "from sqlalchemy.ext.asyncio import create_async_engine\n",
    "import psycopg\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "# 加载环境变量\n",
    "load_dotenv()\n",
    "\n",
    "# 从环境变量获取数据库连接信息，如果不存在则使用默认值\n",
    "driver=os.getenv('DRIVER', 'postgresql+psycopg')\n",
    "user=os.getenv('PGUSER', None)\n",
    "password=os.getenv('PGPASSWORD', None)\n",
    "host=os.getenv('PGHOST', None)\n",
    "port=os.getenv('PGPORT', None)\n",
    "database=os.getenv('PGDATABASE', 'postgres')\n",
    "\n",
    "# schema is used for postgres, similar with database level in MySQL\n",
    "schema=os.getenv('SCHEMA',\"public\")\n",
    "\n",
    "DATABASE_URL = f\"{driver}://{user}:{password}@{host}:{port}/{database}\"\n",
    "\n",
    "if user is not None and password is not None:\n",
    "    print(f\"{driver}://{user}:********@{host}:{port}/{database}\")\n",
    "else:\n",
    "    print('非法的数据库连接URL')\n",
    "    sys.exit(1)\n",
    "print('环境变量加载完成！')\n",
    "\n",
    "# 创建数据库连接引擎\n",
    "try:\n",
    "    engine = create_async_engine(DATABASE_URL)\n",
    "    print('数据库连接引擎创建成功！')\n",
    "except Exception as e:\n",
    "    print(f'创建数据库连接引擎失败: {e}')\n",
    "    raise\n",
    "# 从PostgreSQL数据库读取数据\n",
    "\n",
    "\n",
    "project_name=os.getenv('PROJECT', 'public')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0725b3a8-2327-4f20-90dc-dc3defa1e509",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "sys.path.append('./py')\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from scorpio.core.database import VectorDatabaseManager\n",
    "from scorpio.core.common import DatabaseConfig, EmbeddingConfig, get_logger\n",
    "logger=get_logger(__name__)\n",
    "db_config=DatabaseConfig()\n",
    "db_config\n",
    "embedding_config=EmbeddingConfig()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f18a4f1-1346-4675-9fda-5c256f8ff983",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmbeddingConfig(provider='ollama', base_url='http://localhost:11434', model='mxbai-embed-large:335m', dimension=1024, openai_api_key=None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db_manager=VectorDatabaseManager(db_config)\n",
    "await db_manager.initialize()\n",
    "embedding_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f9eb3e0f-29f8-4653-9dab-a71737fae24f",
   "metadata": {},
   "outputs": [],
   "source": [
    "question=\"What is DeepSeek R1?\"\n",
    "question=\"What is pgvector?\"\n",
    "from scorpio.shared import RetrievalService\n",
    "retrieval_service = RetrievalService(db_manager, \n",
    "                                      embedding_config=embedding_config)\n",
    "retrieval_txt=await retrieval_service.retrieve(question, top_k=5)\n",
    "#retrieval_txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "67b0557d-67df-4ed9-b9e5-0a5ae143f3d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(retrieval_txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ce914e12-a2d3-43cc-9613-ae79713290f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>content</th>\n",
       "      <th>document_title</th>\n",
       "      <th>similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>166</td>\n",
       "      <td># pgvector\\n\\nOpen-source vector similarity se...</td>\n",
       "      <td>README.md</td>\n",
       "      <td>0.721467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id                                            content document_title  \\\n",
       "0  166  # pgvector\\n\\nOpen-source vector similarity se...      README.md   \n",
       "\n",
       "   similarity  \n",
       "0    0.721467  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(retrieval_txt)\n",
    "df.head(20)[['id','content','document_title', 'similarity']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef24343b-0924-458e-8a81-b9f5c0939a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "distance\n",
       "0.278533    1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('distance')['distance'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a15752b-6dbf-4144-98e9-3a0602823e39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== similarity列描述性统计 ===\n",
      "数据数量: 1\n",
      "平均值: 0.7215\n",
      "中位数: 0.7215\n",
      "标准差: nan\n",
      "最小值: 0.7215\n",
      "最大值: 0.7215\n",
      "四分位数 - Q1: 0.7215\n",
      "四分位数 - Q3: 0.7215\n",
      "偏度: nan\n",
      "峰度: nan\n",
      "\n",
      "=== 使用describe()方法 ===\n",
      "count    1.000000\n",
      "mean     0.721467\n",
      "std           NaN\n",
      "min      0.721467\n",
      "25%      0.721467\n",
      "50%      0.721467\n",
      "75%      0.721467\n",
      "max      0.721467\n",
      "Name: similarity, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 基础描述性统计\n",
    "print(\"=== similarity列描述性统计 ===\")\n",
    "print(f\"数据数量: {len(df)}\")\n",
    "print(f\"平均值: {df['similarity'].mean():.4f}\")\n",
    "print(f\"中位数: {df['similarity'].median():.4f}\")\n",
    "print(f\"标准差: {df['similarity'].std():.4f}\")\n",
    "print(f\"最小值: {df['similarity'].min():.4f}\")\n",
    "print(f\"最大值: {df['similarity'].max():.4f}\")\n",
    "print(f\"四分位数 - Q1: {df['similarity'].quantile(0.25):.4f}\")\n",
    "print(f\"四分位数 - Q3: {df['similarity'].quantile(0.75):.4f}\")\n",
    "print(f\"偏度: {df['similarity'].skew():.4f}\")\n",
    "print(f\"峰度: {df['similarity'].kurtosis():.4f}\")\n",
    "\n",
    "# 使用describe()方法\n",
    "print(\"\\n=== 使用describe()方法 ===\")\n",
    "print(df['similarity'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9416ce0f-464e-41b4-8b30-ba1e3938f223",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[df['similarity']>0.7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b5b81d6f-ed22-4719-aaf5-d87a40e63875",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                      1\n",
       "content                 1\n",
       "metadata                1\n",
       "document_title          1\n",
       "external_document_id    1\n",
       "distance                1\n",
       "similarity              1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['similarity']>0.7].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3390a143-f1e3-42e5-b4d7-23be2efb72c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>content</th>\n",
       "      <th>metadata</th>\n",
       "      <th>document_title</th>\n",
       "      <th>external_document_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>166</td>\n",
       "      <td># pgvector\\n\\nOpen-source vector similarity se...</td>\n",
       "      <td>{\"end_char\": 110, \"start_char\": 0}</td>\n",
       "      <td>README.md</td>\n",
       "      <td>a0afa21c-c98e-4fdf-900a-a69bec91df82</td>\n",
       "      <td>0.278533</td>\n",
       "      <td>0.721467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id                                            content  \\\n",
       "0  166  # pgvector\\n\\nOpen-source vector similarity se...   \n",
       "\n",
       "                             metadata document_title  \\\n",
       "0  {\"end_char\": 110, \"start_char\": 0}      README.md   \n",
       "\n",
       "                   external_document_id  distance  similarity  \n",
       "0  a0afa21c-c98e-4fdf-900a-a69bec91df82  0.278533    0.721467  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4eef9424-0ff1-49b0-adc8-b840c0a653f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "async def query(db_manager: VectorDatabaseManager):\n",
    "    \n",
    "    async with db_manager.get_connection() as conn:\n",
    "        base_query = '''\n",
    "                SELECT \n",
    "                    dc.id,\n",
    "                    dc.content,\n",
    "                    dc.metadata,\n",
    "                    dc.document_id as external_document_id,\n",
    "                    dc.embedding\n",
    "                FROM document_chunks dc\n",
    "                WHERE dc.id in ($1)\n",
    "        '''\n",
    "        params=[166]\n",
    "        try:\n",
    "            records = await conn.fetch(base_query, *params)\n",
    "                \n",
    "            logger.info(f\"similarity search record number: {len(records)}. \\n Record content: {[record['content'] for record in records]}\")\n",
    "            return records\n",
    "        except Exception as e:\n",
    "            logger.error(f\"similarity_search query error: {e}\")\n",
    "            raise e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8f2bf0a7-d4b8-4a32-a7d3-d3bbc2718a0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Record id=166 content='# pgvector\\n\\nOpen-source vector similarity search for Postgres\\n\\nStore your vectors with the rest of your data.' metadata='{\"end_char\": 110, \"start_char\": 0}' external_document_id=31 embedding=array([ 0.2524724 ,  0.75938976,  0.574752  , ...,  0.1402977 ,\n",
       "         0.5184065 , -0.26983765], shape=(1024,), dtype=float32)>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records=await query(db_manager)\n",
    "records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fb212c09-d360-4373-a777-24ef8cef4224",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# pgvector\n",
      "\n",
      "Open-source vector similarity search for Postgres\n",
      "\n",
      "Store your vectors with the rest of your data.\n"
     ]
    }
   ],
   "source": [
    "for record in records:\n",
    "    print(record['content'])\n",
    "#records[0]['content']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0f8c44d2-3cbe-441f-8c73-8984984eaaaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmbeddingConfig(provider='ollama', base_url='http://localhost:11434', model='mxbai-embed-large:335m', dimension=1024, openai_api_key=None)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scorpio.core.common import get_logger, EmbeddingConfig\n",
    "embedding_config: EmbeddingConfig = EmbeddingConfig()\n",
    "embedding_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "10f2297a-0a91-4a85-b12e-943773eccaa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import asyncio\n",
    "from typing import List, Dict, Any, Optional\n",
    "from ollama import AsyncClient\n",
    "\n",
    "class OllamaAsyncProvider:\n",
    "    def __init__(self, embedding_config: EmbeddingConfig):\n",
    "        \"\"\"\n",
    "        初始化 Ollama 异步客户端\n",
    "        \n",
    "        Args:\n",
    "            model_param: 模型参数配置\n",
    "        \"\"\"\n",
    "        self.model = embedding_config.model\n",
    "        self.base_url = embedding_config.base_url\n",
    "             \n",
    "        # 初始化异步客户端\n",
    "        self.client = AsyncClient(host=self.base_url)\n",
    "    \n",
    "      \n",
    "    async def validate(self) -> bool:\n",
    "        \"\"\"\n",
    "        验证与 Ollama 服务的连接\n",
    "        \n",
    "        Returns:\n",
    "            bool: 连接是否成功\n",
    "        \"\"\"\n",
    "        try:\n",
    "            await self.client.list()\n",
    "            return True\n",
    "        except Exception as e:\n",
    "            print(f\"连接失败: {e}\")\n",
    "            return False\n",
    "    async def get_embedding(self, text:str):\n",
    "        \"\"\"\n",
    "        批量获取文本嵌入向量\n",
    "        \n",
    "        Args:\n",
    "            texts: 文本列表\n",
    "            \n",
    "        Returns:\n",
    "            嵌入向量列表\n",
    "        \"\"\"\n",
    "        try: \n",
    "            response = await self.client.embed(\n",
    "                model=self.model,\n",
    "                input=text)\n",
    "            print(response.prompt_eval_count, response.eval_count)\n",
    "        except Exception as e:\n",
    "            print(f\"生成嵌入向量失败: {e}\")\n",
    "            # 可以选择返回空向量或重新抛出异常\n",
    "            raise\n",
    "        return response.embeddings\n",
    "    async def get_embeddings(self, texts: List[str]) -> List[List[float]]:\n",
    "        \"\"\"\n",
    "        批量获取文本嵌入向量\n",
    "        \n",
    "        Args:\n",
    "            texts: 文本列表\n",
    "            \n",
    "        Returns:\n",
    "            嵌入向量列表\n",
    "        \"\"\"\n",
    "       \n",
    "        try:\n",
    "            response = await self.client.embed(\n",
    "                model=self.model,\n",
    "                input=texts\n",
    "            )\n",
    "            \n",
    "        except Exception as e:\n",
    "            print(f\"生成多嵌入向量失败: {e}\")\n",
    "            # 可以选择返回空向量或重新抛出异常\n",
    "            raise\n",
    "        \n",
    "        return response.embeddings\n",
    "    \n",
    "    async def get_embeddings_batch(self, texts: List[str], batch_size: int = 10) -> List[List[float]]:\n",
    "        \"\"\"\n",
    "        批量获取嵌入向量（带批处理）\n",
    "        \n",
    "        Args:\n",
    "            texts: 文本列表\n",
    "            batch_size: 批处理大小\n",
    "            \n",
    "        Returns:\n",
    "            嵌入向量列表\n",
    "        \"\"\"\n",
    "        all_embeddings = []\n",
    "        \n",
    "        for i in range(0, len(texts), batch_size):\n",
    "            batch_texts = texts[i:i + batch_size]\n",
    "            batch_embeddings = await self.get_embeddings(batch_texts)\n",
    "            all_embeddings.extend(batch_embeddings)\n",
    "            print(f\"已处理 {min(i + batch_size, len(texts))}/{len(texts)} 个文本\")\n",
    "        \n",
    "        return all_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c959e3d8-36ea-4053-85ee-cc1ed77d9354",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 None\n"
     ]
    }
   ],
   "source": [
    "client=OllamaAsyncProvider(embedding_config)\n",
    "text=\"什么是人工智能?\"\n",
    "query_embeddings=await client.get_embedding(text)\n",
    "#print(len(query_embeddings))\n",
    "#query_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9d6d6657-bcb0-43d4-a90e-0a01610fcdd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16 None\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "texts=[\n",
    "    \"什么是人工智能?\",\n",
    "    \"What is DeepSeek R1?\"\n",
    "]\n",
    "query_embeddings=await client.get_embedding(texts)\n",
    "print(len(query_embeddings))\n",
    "# query_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d4be69b0-f64f-4774-b9c8-0363171570df",
   "metadata": {},
   "outputs": [],
   "source": [
    "#models = await client.client.list()\n",
    "#print(models.model_dump_json(indent=2))\n",
    "show_info=await client.client.show(client.model)\n",
    "#print(show_info.model_dump_json(indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9c01089d-474d-4655-9d7e-e6f3f952016c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.13/site-packages/IPython/extensions/deduperreload/deduperreload.py:290: DeprecationWarning: ast.Ellipsis is deprecated and will be removed in Python 3.14; use ast.Constant instead\n",
      "  elif not isinstance(ast_elt, (ast.Ellipsis, ast.Pass)):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1024"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scorpio.core.providers import OpenAIProvider\n",
    "\n",
    "from scorpio.core.common import ModelParameter\n",
    "\n",
    "model_param=ModelParameter(\n",
    "      model=\"mxbai-embed-large:335m\",\n",
    "      title= \"mxbai-embed\",\n",
    "      provider= \"openai\",\n",
    "      api_key= \"empty\",\n",
    "      base_url= \"http://localhost:11434/v1\",\n",
    "      keep_alive_seconds= \"240\"\n",
    ")\n",
    "cli=OpenAIProvider(model_param)\n",
    "embeddings=await cli.get_embeddings(texts)\n",
    "len(embeddings[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ed767afb-903b-440f-945e-2fb68b7b9687",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 33,
     "metadata": {},
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